{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import fetch_olivetti_faces\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "faces = fetch_olivetti_faces()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'images', 'target', 'DESCR'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faces.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 64, 64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faces.images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14d7f5e29b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14d7f5b3198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(faces.images[10], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14d7f62a5c0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14d7f5cd400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(faces.images[11], cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* One face from each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "testX = faces.data[np.array(list(range(1,400,10))),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_data = faces.data[:,:2048]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(feature_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_data = df.drop(list(range(1,400,10))).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_data = faces.data[:,2048:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(target_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_data = df.drop(list(range(1,400,10))).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.fit(feature_data,target_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 4096)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14d07233ac8>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14d073996d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(testX[0].reshape(64,64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = dt.predict(testX[:,:2048][1:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2048)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = np.append(testX[:,:2048][1:2],pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4096,)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14d0760b7f0>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14d075c5f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(res.reshape(64,64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
